code: def solve_solution(
      gm: ColoGraphModule,
      strategy_constructor: StrategiesConstructor,
      memory_budget: float = -1.0,
  ):
      """
      This method is used to solve the best solution for the given graph.
      The solution is a list of integers, each integer represents the best strategy index of the corresponding node.
      """
      # temporarily we use all nodes as liveness list, we count the backward memory cost together with
      # forward memory cost into the node memory cost, and no activation checkpoint is used in this phase.
      # graph_analyser = GraphAnalyser(gm)
      # liveness_list = graph_analyser.liveness_analysis()
      cost_graph = CostGraph(strategy_constructor.leaf_strategies)
      cost_graph.simplify_graph()
      solver = Solver(
          gm.graph,
          strategy_constructor,
          cost_graph,
          memory_budget=memory_budget,
      )
      ret = solver.call_solver_serialized_args()
      solution = list(ret[0])
  
      return solution
  
imports: from typing import Dict, List, Tuple
  import torch
  import torch.distributed
  import torch.nn
  from torch.fx.graph import Graph
  from colossalai._analyzer.fx.codegen import ActivationCheckpointCodeGen
  from colossalai._analyzer.fx.graph_module import ColoGraphModule
  from colossalai._analyzer.fx.passes import shape_prop_pass
  from colossalai._analyzer.fx.tracer.tracer import ColoTracer
  from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply_pass
  from colossalai.auto_parallel.passes.runtime_preparation_pass import runtime_preparation_pass
  from colossalai.auto_parallel.tensor_shard.options import DataloaderOption, ShardOption, SolverOptions, SolverPerference
  from colossalai.auto_parallel.tensor_shard.sharding_strategy import CommAction
  from colossalai.auto_parallel.tensor_shard.solver import CostGraph, Solver, StrategiesConstructor
  from colossalai.device.alpha_beta_profiler import AlphaBetaProfiler
  from colossalai.device.device_mesh import DeviceMesh
  from colossalai.tensor.sharding_spec import ShardingSpec
label_desc: Use image node_id 4 for calling a global function with example usage: solve_solution(gm, strategy_constructor, memory_budget) and returns: solution